The Application of the Combinatorial Optimization Problems Based on Preventive Feedback Pulse Coupled Neural Network
Intelligent Systems and Applications（2011）
摘要
Pulse Coupled Neural Network (PCNN) with the phenomena of synchronous pulse bursts is different from traditional artificial neural networks. In this paper, the auto-wave in PCNN is used to solve combination optimization problems. The preventive feedback based on triangle inequality theorem is introduced to prevent bad solutions, and Preventive Feedback Pulse Coupled Neural Network (PFPCNN) is presented. In the process of searching solutions, the solution space complexity of combinatorial optimization problems is reduced and the efficiency and accuracy is improved. This algorithm is applied to SP, TSP simulation. The results show that the algorithm can effectively reduce space complexity and improve the searching speed further.The method based on auto-wave to solve combination optimization problems is a more quickly, more stable.
更多查看译文
关键词
combinatorial optimization,traveling salesman problem,pulse coupled neural network,travelling salesman problems,combinatorial optimization problem,auto-wave,combinatorial mathematics,triangle inequality theorem,search problems,sp simulation,computational complexity,solution space complexity,preventive feedback,searching solutions,preventive feedback pulse coupled neural network,recurrent neural nets,synchronous pulse bursts,tsp simulation,autowave characteristics,shortest path problem,artificial neural networks,optimization,mathematical model,algorithm design and analysis
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要